How do you keep replayed tests trustworthy over time as dependencies and schemas evolve? (i.e. without turning into brittle snapshot tests)
Also, how do you normalize non-determinism (like time/IDs etc.), expire/refresh recordings, and classify diffs as "intentional change" vs "regression"?
Have been building Saleswhale (https://www.saleswhale.com/) for a while now - we got to >$1M in annual revenues mostly selling to the mid-market, but decided to launch a Free Tier this year.
The problem we are solving is helping marketers turn more underserved leads into revenue through an email AI assistant.
I think he's talking in absolute terms versus relative terms that founders are used to thinking in.
Imagine that you have $3 million in runway, but you are burning $500K a month. That only gives you 6 months of runway.
It takes perspective to zoom out to realise - "Hey, I actually have $3 million in the bank. That's a lot of money. If I can somehow reduce costs to $150K a month, I actually have 20 months of runway to figure things out."
Unfortunately, this means "cutting more than needed".
I am a software engineer who learnt how to love sales and marketing.
A few months ago, I decided to reach out personally to a few “stale leads” in our database to audit our processes, and manually check if our automation systems were functioning normally.
I got more than a handful of emails responding with replies like -
"Yes, I reached out in April as the team was interested in Saleswhale. But we didn't hear back from your sales team. That said, the team couldn't wait to implement a solution, and have went with another similar vendor."
Which was alarming, to say the least.
So, I wrote a short article chronicling our struggles with the "nurture gap", and how we eventually solved this problem.. with engineering ;)
Hey there. I took a look at your landing page and it's an interesting premise. We are also in alpha stage right now for Saleswhale (http://saleswhale.io) since 4 months ago, and I've learnt alot about the process.
One thing I've learnt is to look at the pre-public phase (alpha, beta) as a continuum rather than as a hard milestone. The whole purpose of this phase is validated learning, and to make something that people love. Growth is generally not important to us now, although we are still tracking lead velocity (number of inbound email signups weekly).
Not pursuing more users right now at the expense of product development and customer development is generally the smart move, provided your initial set of alpha users are well qualified, and are the right target market for your offering. Reason is that you really don't want too much noise to signal ratio, and too many unqualified users will cause your product to be dragged into different directions or trying to be everything to everyone.
Just to go into specifics, we currently have over 800 alpha signups, and we are rolling out by cohorts weekly to different groups of alpha users, which we are prioritising by a weighted heuristic on how long ago they signed up and potential fit based on their industry and role for our software.
We are tracking engagement aggressively - every single click, action and event gets pulled into our analytics database, and we have a single most important metric that we are tracking - number of interactions a day per user. You may have your own metric that you are tracking. Until we see this metric improve exponentially month on month, we will continue to stay in alpha/beta to work on improving the product and features rather than launch to the public. Because what's the point of getting so many users, if they won't engage and you can't keep them?
Hope that helps, and all the best for your startup!
Hi there.. it's really interesting because that's a core feature that we are focusing on at Saleswhale (http://saleswhale.io/product-tour). When you click into a contact's dossier (sadly, I'm not able to upload screenshots here), it will show you all related contacts, interactions and other smart insights that we managed to surface.
We are currently still in beta though, but we would love to work closely with you and get your ideas and feedback how to make Saleswhale more useful for you.
I'm the technical co-founder at Saleswhale and you can drop me a line at [email protected] if you want to chat more.
Very cool! I can totally see where you are coming from. I also tend to get stuck in the minutiae of software sometimes, and it may be empirical data at best, but I feel that women are sometimes so much better at seeing things from the user's point of view, and have a better sense of empathy for the user.
I'm glad to see that you managed to have such a close and complimentary working relationship with ur SO. All the best for your new startup, and am looking forward to Chapter 2 of your blog post!
Hi Nate, thanks for sharing your story. I think it's interesting because my co-founder is also my wife. And it's an interesting dynamic to say the least. If you don't mind me asking, what are the roles that you play individually? Just curious ;)
For myself, I'm the backend engineer and devops, while my wife does the design and front-end engineering.
"There is an epidemic failure within the game to understand what is really happening. And this leads people who run Major League Baseball teams to misjudge their players and mismanage their teams. People who run ball clubs, they think in terms of buying players. Your goal shouldn't be to buy players, your goal should be to buy wins." - Peter Brand
Who were your typical Salesforce consulting and custom development clients? I may be wrong, but I would think that if you have enough money to spend on customisation for an app that doesn't even belong to you, it may be cheaper and more efficient in the long run to commission a custom built solution.
What business are you running, and how big is your current team? Just curious, as I've heard that Salesforce is probably overkill for most small businesses/startups.
Also, how do you normalize non-determinism (like time/IDs etc.), expire/refresh recordings, and classify diffs as "intentional change" vs "regression"?